From 838aa7044d625d7a4d876fc8580a423fbf324f33 Mon Sep 17 00:00:00 2001
From: cmoineau <cyril.moineau@cea.fr>
Date: Thu, 10 Oct 2024 06:37:54 +0000
Subject: [PATCH] Update export cpp with new registry methods.
---
aidge_export_cpp/export_registry.py | 7 +-
aidge_export_cpp/kernels/activation.hpp | 30 ++---
aidge_export_cpp/kernels/batchnorm.hpp | 8 +-
aidge_export_cpp/kernels/convolution.hpp | 26 ++---
aidge_export_cpp/kernels/fullyconnected.hpp | 16 +--
aidge_export_cpp/operators.py | 103 +++++-------------
.../configuration/activation_config.jinja | 1 +
.../configuration/convolution_config.jinja | 2 +-
.../configuration/elemwise_config.jinja | 1 +
.../configuration/fullyconnected_config.jinja | 2 +-
.../templates/network/environment.jinja | 25 -----
.../templates/network/network_forward.jinja | 36 ------
12 files changed, 78 insertions(+), 179 deletions(-)
delete mode 100644 aidge_export_cpp/templates/network/environment.jinja
delete mode 100644 aidge_export_cpp/templates/network/network_forward.jinja
diff --git a/aidge_export_cpp/export_registry.py b/aidge_export_cpp/export_registry.py
index fe7e80c..7f10eea 100644
--- a/aidge_export_cpp/export_registry.py
+++ b/aidge_export_cpp/export_registry.py
@@ -2,9 +2,14 @@ from aidge_core.export_utils import ExportLib
from aidge_export_cpp.utils import ROOT
class ExportLibCpp(ExportLib):
- name="export_cpp"
+ _name="export_cpp"
static_files={
str(ROOT / "static" / "Makefile"): "",
str(ROOT / "static" / "include" / "network" / "typedefs.hpp"): "dnn/include/network",
str(ROOT / "static" / "include" / "network" / "utils.hpp"): "dnn/include/network",
}
+
+# TODO ugly fix for Tensor registration issue...
+import aidge_core
+aidge_core.register_Tensor(["export_cpp", aidge_core.dtype.float32],
+ aidge_core.get_key_value_Tensor(["cpu", aidge_core.dtype.float32]))
diff --git a/aidge_export_cpp/kernels/activation.hpp b/aidge_export_cpp/kernels/activation.hpp
index b5fbd61..d669515 100644
--- a/aidge_export_cpp/kernels/activation.hpp
+++ b/aidge_export_cpp/kernels/activation.hpp
@@ -4,36 +4,36 @@
#include <type_traits>
#include "network/typedefs.hpp"
#include "network/utils.hpp"
-#include "network/rescaling.hpp"
+#include "kernels/rescaling.hpp"
-template<typename Output_T, typename T,
+template<typename Output_T, typename T,
typename std::enable_if<std::is_floating_point<T>::value>::type* = nullptr>
__attribute__((always_inline)) inline
-Output_T saturate (T value, int32_t /*sat*/)
+Output_T saturate (T value, int32_t /*sat*/)
{
return value;
}
-template<typename Output_T, typename T,
+template<typename Output_T, typename T,
typename std::enable_if<!std::is_floating_point<T>::value>::type* = nullptr>
__attribute__((always_inline)) inline
-Output_T saturate (T value, uint32_t sat)
+Output_T saturate (T value, uint32_t sat)
{
if (std::is_unsigned<Output_T>::value) {
return clamp(value, T(0), (T(1) << sat) - 1);
} else {
return clamp(value, -(T(1) << (sat - 1)), (T(1) << (sat - 1)) - 1);
- }
+ }
}
-template<typename Output_T,
- typename Sum_T,
+template<typename Output_T,
+ typename Sum_T,
typename Rescaling_T>
-__attribute__((always_inline)) inline
-Output_T activation_forward_value (Sum_T weightedSum,
- int output,
- ActivationFunction_T func,
- const Rescaling_T& __restrict rescaling)
+__attribute__((always_inline)) inline
+Output_T activation_forward_value (Sum_T weightedSum,
+ int output,
+ ActivationFunction_T func,
+ const Rescaling_T& __restrict rescaling)
{
switch(func) {
case Linear:
@@ -49,7 +49,7 @@ Output_T activation_forward_value (Sum_T weightedSum,
break;
}
- // Value fixed here for now but it should be generated by
+ // Value fixed here for now but it should be generated by
// the export module or determined by the type of Output_T
// For now only works for int8_t and uint8_t
const uint32_t NB_BITS = 8;
@@ -60,7 +60,7 @@ Output_T activation_forward_value (Sum_T weightedSum,
template<int NB_DATA,
ActivationFunction_T ACTIVATION,
typename Input_T, typename Output_T, typename Rescaling_T>
-__attribute__((always_inline)) inline
+__attribute__((always_inline)) inline
void activation_forward (
const Input_T* __restrict inputs,
Output_T* __restrict outputs,
diff --git a/aidge_export_cpp/kernels/batchnorm.hpp b/aidge_export_cpp/kernels/batchnorm.hpp
index d63c961..740ea21 100644
--- a/aidge_export_cpp/kernels/batchnorm.hpp
+++ b/aidge_export_cpp/kernels/batchnorm.hpp
@@ -2,17 +2,17 @@
#define __AIDGE_EXPORT_CPP_KERNELS_BATCHNORM__
#include "network/typedefs.hpp"
-#include "network/rescaling.hpp"
+#include "kernels/rescaling.hpp"
#include <math.h>
// WARNING: this kernel only works for 32-bits floating point values
-template<int NB_OUTPUTS,
+template<int NB_OUTPUTS,
int OUTPUTS_HEIGHT, int OUTPUTS_WIDTH,
ActivationFunction_T ACTIVATION,
- typename Input_T, typename Output_T,
+ typename Input_T, typename Output_T,
typename Param_T>
-__attribute__((always_inline)) inline
+__attribute__((always_inline)) inline
void batchnorm_forward (
const Input_T* __restrict inputs,
Output_T* __restrict outputs,
diff --git a/aidge_export_cpp/kernels/convolution.hpp b/aidge_export_cpp/kernels/convolution.hpp
index 39f931a..efc7ee7 100644
--- a/aidge_export_cpp/kernels/convolution.hpp
+++ b/aidge_export_cpp/kernels/convolution.hpp
@@ -2,13 +2,13 @@
#define __AIDGE_EXPORT_CPP_KERNELS_CONVOLUTION__
#include "network/typedefs.hpp"
-#include "network/rescaling.hpp"
+#include "kernels/rescaling.hpp"
#include "network/utils.hpp"
#include "kernels/macs.hpp"
#include "kernels/activation.hpp"
-template<int NB_CHANNELS,
+template<int NB_CHANNELS,
int CHANNELS_HEIGHT, int CHANNELS_WIDTH,
int NB_OUTPUTS,
int OUTPUTS_HEIGHT, int OUTPUTS_WIDTH,
@@ -17,10 +17,10 @@ template<int NB_CHANNELS,
int DILATION_Y, int DILATION_X,
int KERNEL_HEIGHT, int KERNEL_WIDTH,
ActivationFunction_T ACTIVATION,
- typename Input_T, typename Output_T,
+ typename Input_T, typename Output_T,
typename Weight_T, typename Bias_T,
typename Rescaling_T>
-__attribute__((always_inline)) inline
+__attribute__((always_inline)) inline
void convolution_forward(
const Input_T* __restrict inputs,
Output_T* __restrict outputs,
@@ -28,10 +28,10 @@ void convolution_forward(
const Bias_T* __restrict biases,
const Rescaling_T& __restrict rescaling)
{
- constexpr int DILATED_KERNEL_HEIGHT
+ constexpr int DILATED_KERNEL_HEIGHT
= KERNEL_HEIGHT + (DILATION_Y - 1) * (KERNEL_HEIGHT - 1);
- constexpr int DILATED_KERNEL_WIDTH
+ constexpr int DILATED_KERNEL_WIDTH
= KERNEL_WIDTH + (DILATION_X - 1) * (KERNEL_WIDTH - 1);
constexpr int OUTPUTS_HEIGHT_NOPAD
@@ -44,7 +44,7 @@ void convolution_forward(
: max(PADDING_Y - (oy * STRIDE_Y), 0);
const int syMax = (PADDING_Y == 0
&& OUTPUTS_HEIGHT == OUTPUTS_HEIGHT_NOPAD) ? DILATED_KERNEL_HEIGHT
- : clamp(CHANNELS_HEIGHT + PADDING_Y - (oy * STRIDE_Y),
+ : clamp(CHANNELS_HEIGHT + PADDING_Y - (oy * STRIDE_Y),
0, DILATED_KERNEL_HEIGHT);
const int iy = (oy * STRIDE_Y) - PADDING_Y;
@@ -57,7 +57,7 @@ void convolution_forward(
const int sxMax = (PADDING_X == 0
&& OUTPUTS_WIDTH == OUTPUTS_WIDTH_NOPAD)
? DILATED_KERNEL_WIDTH
- : clamp(CHANNELS_WIDTH + PADDING_X - (ox * STRIDE_X),
+ : clamp(CHANNELS_WIDTH + PADDING_X - (ox * STRIDE_X),
0, DILATED_KERNEL_WIDTH);
const int ix = (ox * STRIDE_X) - PADDING_X;
@@ -85,8 +85,8 @@ void convolution_forward(
|| sxMax - sxMin == KERNEL_WIDTH))
{
macsOnRange<KERNEL_WIDTH * NB_CHANNELS>(
- inputs + iOffset,
- weights + wOffset,
+ inputs + iOffset,
+ weights + wOffset,
weightedSum);
}
else {
@@ -100,11 +100,11 @@ void convolution_forward(
int iOffsetInRange = iOffset
+ sx * DILATION_X * NB_CHANNELS;
-
+
macsOnRange<NB_CHANNELS>(
// same input line so no wrapping can occur
- inputs + iOffsetInRange,
- weights + wOffset + sx * NB_CHANNELS,
+ inputs + iOffsetInRange,
+ weights + wOffset + sx * NB_CHANNELS,
weightedSum);
}
}
diff --git a/aidge_export_cpp/kernels/fullyconnected.hpp b/aidge_export_cpp/kernels/fullyconnected.hpp
index 92aef15..895ed1c 100644
--- a/aidge_export_cpp/kernels/fullyconnected.hpp
+++ b/aidge_export_cpp/kernels/fullyconnected.hpp
@@ -2,20 +2,20 @@
#define __AIDGE_EXPORT_CPP_KERNELS_FULLYCONNECTED__
#include "network/typedefs.hpp"
-#include "network/rescaling.hpp"
+#include "kernels/rescaling.hpp"
#include "network/utils.hpp"
#include "kernels/macs.hpp"
#include "kernels/activation.hpp"
-template<int NB_CHANNELS,
+template<int NB_CHANNELS,
int CHANNELS_HEIGHT, int CHANNELS_WIDTH,
int NB_OUTPUTS,
int OUTPUTS_HEIGHT, int OUTPUTS_WIDTH,
ActivationFunction_T ACTIVATION,
- typename Input_T, typename Output_T,
+ typename Input_T, typename Output_T,
typename Weight_T, typename Bias_T,
typename Rescaling_T>
-__attribute__((always_inline)) inline
+__attribute__((always_inline)) inline
void fullyconnected_forward (
const Input_T* __restrict inputs,
Output_T* __restrict outputs,
@@ -35,7 +35,7 @@ void fullyconnected_forward (
for (int iy = 0; iy < CHANNELS_HEIGHT; ++iy) {
for (int ix = 0; ix < CHANNELS_WIDTH; ++ix) {
for (int ch = 0; ch < NB_CHANNELS; ++ch) {
- weightedSum += inputs[CHANNELS_WIDTH*NB_CHANNELS*iy + NB_CHANNELS*ix + ch]
+ weightedSum += inputs[CHANNELS_WIDTH*NB_CHANNELS*iy + NB_CHANNELS*ix + ch]
* weights[CHANNELS_HEIGHT*CHANNELS_WIDTH*NB_CHANNELS*och + CHANNELS_HEIGHT*CHANNELS_WIDTH*ch + CHANNELS_HEIGHT*iy + ix];
}
}
@@ -58,8 +58,8 @@ Here the kernel to use with inputs in NHWC and weights in NHWC
* (iy + CHANNELS_HEIGHT * och);
macsOnRange<NB_CHANNELS * CHANNELS_WIDTH>(
- inputs + iOffset,
- weights + wOffset,
+ inputs + iOffset,
+ weights + wOffset,
weightedSum);
}
@@ -69,4 +69,4 @@ Here the kernel to use with inputs in NHWC and weights in NHWC
}
-#endif // __AIDGE_EXPORT_CPP_KERNELS_FULLYCONNECTED__
\ No newline at end of file
+#endif // __AIDGE_EXPORT_CPP_KERNELS_FULLYCONNECTED__
diff --git a/aidge_export_cpp/operators.py b/aidge_export_cpp/operators.py
index b31fe85..8bdc421 100644
--- a/aidge_export_cpp/operators.py
+++ b/aidge_export_cpp/operators.py
@@ -1,31 +1,11 @@
import os
import numpy as np
from pathlib import Path
-from aidge_core.export_utils import ExportNode, ExportNodeCpp, operator_register, generate_file
+import aidge_core
+from aidge_core.export_utils import ExportNode, ExportNodeCpp, generate_file
from aidge_export_cpp.utils import ROOT
from aidge_export_cpp.utils.converter import numpy_dtype2ctype
-from aidge_export_cpp.utils.generation import *
from aidge_export_cpp import ExportLibCpp
-##############################################
-################### Utils ####################
-##############################################
-
-
-# def get_node_parents(node):
-# parents = []
-# for parent in node.get_parents():
-# if parent.type() != "Producer":
-# parents.append(parent)
-# return parents
-
-
-# def get_producer_parents(node):
-# parents = []
-# for parent in node.get_parents():
-# if parent.type() == "Producer":
-# parents.append(parent)
-# return parents
-
##############################################
############## Export functions ##############
@@ -55,10 +35,8 @@ def export_params(name: str,
############## Operators helper ##############
##############################################
-
-@operator_register(ExportLibCpp, "Producer")
+@ExportLibCpp.register("Producer", aidge_core.ImplSpec(aidge_core.IOSpec(aidge_core.dtype.any)))
class ProducerCPP(ExportNode):
-
def __init__(self, node, mem_info, is_input, is_output):
super().__init__(node, mem_info, is_input, is_output)
self.values = np.array(self.operator.get_output(0))
@@ -77,11 +55,15 @@ class ProducerCPP(ExportNode):
def forward(self):
# A Producer does nothing during forward
return []
- @classmethod
- def exportable(cls, node):
- return True # TODO add check i/o NCHW
-@operator_register(ExportLibCpp, "ReLU")
+# TODO : find a way to remove this dummy exportnode
+@ExportLibCpp.register("Pad2D", aidge_core.ImplSpec(aidge_core.IOSpec(aidge_core.dtype.any)))
+class Pad_ARMCortexM(ExportNodeCpp):
+ def __init__(self, node, mem_info, is_input, is_output):
+ raise NotImplementedError("Pad2D nodes is not implemented")
+
+
+@ExportLibCpp.register("ReLU", aidge_core.ImplSpec(aidge_core.IOSpec(aidge_core.dtype.float32)))
class ReLUCPP(ExportNodeCpp):
def __init__(self, node, mem_info, is_input, is_output):
super().__init__(node, mem_info, is_input, is_output)
@@ -97,11 +79,7 @@ class ReLUCPP(ExportNodeCpp):
str(ROOT / "kernels" / "rescaling.hpp")
]
- @classmethod
- def exportable(cls, node):
- return True # TODO add check i/o NCHW
-
-@operator_register(ExportLibCpp, "Conv")
+@ExportLibCpp.register("Conv2D", aidge_core.ImplSpec(aidge_core.IOSpec(aidge_core.dtype.float32)))
class ConvCPP(ExportNodeCpp):
def __init__(self, node, mem_info, is_input, is_output):
super().__init__(node, mem_info, is_input, is_output)
@@ -121,22 +99,17 @@ class ConvCPP(ExportNodeCpp):
str(ROOT / "kernels" / "activation.hpp"),
str(ROOT / "kernels" / "rescaling.hpp")
]
- @classmethod
- def exportable(cls, node):
- return True # TODO add check i/o NCHW
-
-
-@operator_register(ExportLibCpp, "PaddedConv")
+@ExportLibCpp.register_metaop("PaddedConv2D", aidge_core.ImplSpec(aidge_core.IOSpec(aidge_core.dtype.float32)))
class PaddedConvCPP(ExportNodeCpp):
def __init__(self, node, mem_info, is_input, is_output):
super().__init__(node, mem_info, is_input, is_output)
# TODO find a way to retrive attr for meta op
for n in self.operator.get_micro_graph().get_nodes():
- if n.type() == "Pad":
+ if n.type() == "Pad2D":
self.attributes["padding"] = n.get_operator(
).attr.begin_end_borders
- if n.type() == "Conv":
+ if n.type() == "Conv2D":
self.attributes["kernel_dims"] = n.get_operator(
).attr.kernel_dims
self.attributes["stride_dims"] = n.get_operator(
@@ -156,11 +129,8 @@ class PaddedConvCPP(ExportNodeCpp):
str(ROOT / "kernels" / "activation.hpp"),
str(ROOT / "kernels" / "rescaling.hpp")
]
- @classmethod
- def exportable(cls, node):
- return True # TODO add check i/o NCHW
-@operator_register(ExportLibCpp, "Add")
+@ExportLibCpp.register("Add", aidge_core.ImplSpec(aidge_core.IOSpec(aidge_core.dtype.float32)))
class AddCPP(ExportNodeCpp):
def __init__(self, node, mem_info, is_input, is_output):
super().__init__(node, mem_info, is_input, is_output)
@@ -177,11 +147,8 @@ class AddCPP(ExportNodeCpp):
str(ROOT / "kernels" / "activation.hpp"),
str(ROOT / "kernels" / "rescaling.hpp")
]
- @classmethod
- def exportable(cls, node):
- return True # TODO add check i/o NCHW
-@operator_register(ExportLibCpp, "Sub")
+@ExportLibCpp.register("Sub", aidge_core.ImplSpec(aidge_core.IOSpec(aidge_core.dtype.float32)))
class SubCPP(ExportNodeCpp):
def __init__(self, node, mem_info, is_input, is_output):
super().__init__(node, mem_info, is_input, is_output)
@@ -198,12 +165,9 @@ class SubCPP(ExportNodeCpp):
str(ROOT / "kernels" / "activation.hpp"),
str(ROOT / "kernels" / "rescaling.hpp")
]
- @classmethod
- def exportable(cls, node):
- return True # TODO add check i/o NCHW
-@operator_register(ExportLibCpp, "Mul")
+@ExportLibCpp.register("Mul", aidge_core.ImplSpec(aidge_core.IOSpec(aidge_core.dtype.float32)))
class MulCPP(ExportNodeCpp):
def __init__(self, node, mem_info, is_input, is_output):
super().__init__(node, mem_info, is_input, is_output)
@@ -220,11 +184,8 @@ class MulCPP(ExportNodeCpp):
str(ROOT / "kernels" / "activation.hpp"),
str(ROOT / "kernels" / "rescaling.hpp")
]
- @classmethod
- def exportable(cls, node):
- return True # TODO add check i/o NCHW
-@operator_register(ExportLibCpp, "MaxPooling")
+@ExportLibCpp.register("MaxPooling2D", aidge_core.ImplSpec(aidge_core.IOSpec(aidge_core.dtype.float32)))
class MaxPoolCPP(ExportNodeCpp):
def __init__(self, node, mem_info, is_input, is_output):
super().__init__(node, mem_info, is_input, is_output)
@@ -243,21 +204,19 @@ class MaxPoolCPP(ExportNodeCpp):
self.kernels_to_copy = [
str(ROOT / "kernels" / "pooling.hpp"),
str(ROOT / "kernels" / "activation.hpp"),
+ str(ROOT / "kernels" / "rescaling.hpp")
]
- @classmethod
- def exportable(cls, node):
- return True # TODO add check i/o NCHW
-@operator_register(ExportLibCpp, "PaddedMaxPooling")
+@ExportLibCpp.register_metaop("PaddedMaxPooling2D", aidge_core.ImplSpec(aidge_core.IOSpec(aidge_core.dtype.float32)))
class PaddedMaxPoolCPP(ExportNodeCpp):
def __init__(self, node, mem_info, is_input, is_output):
super().__init__(node, mem_info, is_input, is_output)
for n in self.operator.get_micro_graph().get_nodes():
- if n.type() == "Pad":
+ if n.type() == "Pad2D":
self.attributes["padding"] = n.get_operator(
).attr.begin_end_borders
- if n.type() == "MaxPooling":
+ if n.type() == "MaxPooling2D":
self.attributes["kernel_dims"] = n.get_operator(
).attr.kernel_dims
self.attributes["stride_dims"] = n.get_operator(
@@ -273,12 +232,11 @@ class PaddedMaxPoolCPP(ExportNodeCpp):
self.kernels_to_copy = [
str(ROOT / "kernels" / "pooling.hpp"),
str(ROOT / "kernels" / "activation.hpp"),
+ str(ROOT / "kernels" / "rescaling.hpp")
]
- @classmethod
- def exportable(cls, node):
- return True # TODO add check i/o NCHW
-@operator_register(ExportLibCpp, "GlobalAveragePooling")
+
+@ExportLibCpp.register("GlobalAveragePooling", aidge_core.ImplSpec(aidge_core.IOSpec(aidge_core.dtype.float32)))
class GlobalAveragePoolCPP(ExportNodeCpp):
def __init__(self, node, mem_info, is_input, is_output):
super().__init__(node, mem_info, is_input, is_output)
@@ -302,12 +260,10 @@ class GlobalAveragePoolCPP(ExportNodeCpp):
self.kernels_to_copy = [
str(ROOT / "kernels" / "pooling.hpp"),
str(ROOT / "kernels" / "activation.hpp"),
+ str(ROOT / "kernels" / "rescaling.hpp")
]
- @classmethod
- def exportable(cls, node):
- return True # TODO add check i/o NCHW
-@operator_register(ExportLibCpp, "FC")
+@ExportLibCpp.register("FC", aidge_core.ImplSpec(aidge_core.IOSpec(aidge_core.dtype.float32)))
class FcCPP(ExportNodeCpp):
def __init__(self, node, mem_info, is_input, is_output):
super().__init__(node, mem_info, is_input, is_output)
@@ -324,6 +280,3 @@ class FcCPP(ExportNodeCpp):
str(ROOT / "kernels" / "activation.hpp"),
str(ROOT / "kernels" / "rescaling.hpp")
]
- @classmethod
- def exportable(cls, node):
- return True # TODO add check i/o NCHW
diff --git a/aidge_export_cpp/templates/configuration/activation_config.jinja b/aidge_export_cpp/templates/configuration/activation_config.jinja
index f9535e7..1ab5b21 100644
--- a/aidge_export_cpp/templates/configuration/activation_config.jinja
+++ b/aidge_export_cpp/templates/configuration/activation_config.jinja
@@ -1,6 +1,7 @@
{#- For name header -#}
#ifndef {{ name|upper }}_LAYER_H
#define {{ name|upper }}_LAYER_H
+#include "kernels/rescaling.hpp"
{# For layer configuration -#}
{%- set nb_data = in_chan[0] * in_height[0] * in_width[0] %}
diff --git a/aidge_export_cpp/templates/configuration/convolution_config.jinja b/aidge_export_cpp/templates/configuration/convolution_config.jinja
index c536a48..a4a2462 100644
--- a/aidge_export_cpp/templates/configuration/convolution_config.jinja
+++ b/aidge_export_cpp/templates/configuration/convolution_config.jinja
@@ -1,7 +1,7 @@
{#- For name header -#}
#ifndef {{ name|upper }}_LAYER_H
#define {{ name|upper }}_LAYER_H
-
+#include "kernels/rescaling.hpp"
{# For layer configuration -#}
{% include "./_def_io.jinja" %}
{% include "./_meminfo.jinja" %}
diff --git a/aidge_export_cpp/templates/configuration/elemwise_config.jinja b/aidge_export_cpp/templates/configuration/elemwise_config.jinja
index fc801f1..91a0be4 100644
--- a/aidge_export_cpp/templates/configuration/elemwise_config.jinja
+++ b/aidge_export_cpp/templates/configuration/elemwise_config.jinja
@@ -1,6 +1,7 @@
{#- For name header -#}
#ifndef {{ name|upper }}_LAYER_H
#define {{ name|upper }}_LAYER_H
+#include "kernels/rescaling.hpp"
{% include "./_def_io.jinja" %}
{% include "./_meminfo.jinja" %}
diff --git a/aidge_export_cpp/templates/configuration/fullyconnected_config.jinja b/aidge_export_cpp/templates/configuration/fullyconnected_config.jinja
index 0546304..3c80338 100644
--- a/aidge_export_cpp/templates/configuration/fullyconnected_config.jinja
+++ b/aidge_export_cpp/templates/configuration/fullyconnected_config.jinja
@@ -1,7 +1,7 @@
{#- For name header -#}
#ifndef {{ name|upper }}_LAYER_H
#define {{ name|upper }}_LAYER_H
-
+#include "kernels/rescaling.hpp"
{# For layer configuration -#}
{% include "./_def_io.jinja" %}
{% include "./_meminfo.jinja" %}
diff --git a/aidge_export_cpp/templates/network/environment.jinja b/aidge_export_cpp/templates/network/environment.jinja
deleted file mode 100644
index ced5991..0000000
--- a/aidge_export_cpp/templates/network/environment.jinja
+++ /dev/null
@@ -1,25 +0,0 @@
-#ifndef ENV_LAYER_H
-#define ENV_LAYER_H
-
-#include <stdint.h>
-
-#define ENV_SIZE_X {{ size_x }}
-#define ENV_SIZE_Y {{ size_y }}
-#define ENV_NB_OUTPUTS {{ nb_outputs }}
-
-#define ENV_DATA_UNSIGNED {{ is_unsigned }}
-
-#define ENV_OUTPUTS_SIZE (ENV_NB_OUTPUTS*ENV_SIZE_X*ENV_SIZE_Y)
-
-#define NETWORK_TARGETS 1
-//Output targets network dimension definition:
-static unsigned int OUTPUTS_HEIGHT[NETWORK_TARGETS] = {1};
-static unsigned int OUTPUTS_WIDTH[NETWORK_TARGETS] = {1};
-static unsigned int NB_OUTPUTS[NETWORK_TARGETS] = {1000};
-static unsigned int NB_TARGET[NETWORK_TARGETS] = {1000};
-static unsigned int OUTPUTS_SIZE[NETWORK_TARGETS] = {(OUTPUTS_WIDTH[0]*OUTPUTS_HEIGHT[0])};
-typedef int32_t Target_0_T;
-typedef Target_0_T Target_T;
-
-
-#endif // ENV_LAYER_H
diff --git a/aidge_export_cpp/templates/network/network_forward.jinja b/aidge_export_cpp/templates/network/network_forward.jinja
deleted file mode 100644
index 592e8c9..0000000
--- a/aidge_export_cpp/templates/network/network_forward.jinja
+++ /dev/null
@@ -1,36 +0,0 @@
-{#- For libraries #}
-
-#include <stdint.h>
-
-#ifdef SAVE_OUTPUTS
-#include <sys/types.h>
-#include <sys/stat.h>
-#endif
-
-#include "network/rescaling.hpp"
-
-// Layer & memory configurations
-{%- for header in headers %}
-#include "{{ header }}"
-{%- endfor %}
-
-{# mem has the datatype of the firt output #}
-{#- Change here to improve it -#}
-static {{outputs[0][0]}} mem[{{peak_mem}}];
-
-{# Forward function #}
-{#- Support multiple inputs with different datatypes and multiple outputs with different datatypes -#}
-void model_forward({% for inp in inputs %}const {{inp[0]}}* {{inp[1]}}, {% endfor %}{% for out in outputs %}{{out[0]}}* {{out[1]}}{{ ", " if not loop.last else "" }}{% endfor %})
-{
-
- #ifdef SAVE_OUTPUTS
- // Creation of the outputs directory
- struct stat st {};
- if (stat("outputs", &st) == -1) {
- mkdir("outputs", 0700);
- }
- #endif
- {%- for action in actions %}
- {{ action }}
- {%- endfor %}
-}
--
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